Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	126,488
2	South Dakota	120,272
3	Utah	103,079
4	Rhode Island	102,690
5	Tennessee	100,310
6	Wisconsin	98,874
7	Iowa	98,394
8	Arizona	96,617
9	Nebraska	95,977
10	Arkansas	92,524
11	Oklahoma	91,748
12	Kansas	91,497
13	Indiana	89,866
14	Idaho	88,636
15	Alabama	88,215
16	Wyoming	87,124
17	Mississippi	87,064
18	Nevada	86,572
19	Illinois	85,939
20	Montana	84,902
21	Louisiana	81,379
22	Minnesota	79,939
23	New Mexico	79,559
24	California	78,854
25	South Carolina	78,561
26	Missouri	77,599
27	Georgia	77,279
28	Kentucky	76,402
29	Texas	76,217
30	Florida	75,141
31	Delaware	74,476
32	New Jersey	72,750
33	Ohio	72,691
34	Alaska	71,467
35	Massachusetts	70,537
36	North Carolina	66,678
37	New York	66,516
38	Colorado	66,493
39	Connecticut	66,136
40	West Virginia	62,839
41	Pennsylvania	62,070
42	Michigan	59,198
43	Maryland	55,365
44	Virginia	53,846
45	District of Columbia	49,042
46	New Hampshire	44,343
47	Washington	39,451
48	Puerto Rico	38,307
49	Oregon	32,238
50	Maine	26,512
51	Hawaii	17,418
52	Vermont	16,955

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Rhode Island	1,366
2	Arizona	1,097
3	South Carolina	843
4	Texas	821
5	Arkansas	768
6	Georgia	759
7	North Carolina	731
8	California	706
9	Kentucky	698
10	New York	646
11	Massachusetts	616
12	Louisiana	599
13	New Hampshire	595
14	Mississippi	580
15	Alabama	578
16	Utah	577
17	New Jersey	567
18	Nebraska	562
19	Delaware	552
20	Florida	537
21	Ohio	531
22	Connecticut	530
23	Oklahoma	524
24	West Virginia	522
25	Idaho	514
26	Virginia	509
27	Tennessee	494
28	Pennsylvania	472
29	Kansas	462
30	Iowa	460
31	Indiana	457
32	Maine	436
33	Wyoming	412
34	Nevada	408
35	New Mexico	406
36	Illinois	375
37	Missouri	375
38	Wisconsin	363
39	Montana	361
40	Maryland	347
41	Washington	292
42	South Dakota	279
43	District of Columbia	273
44	Colorado	266
45	Michigan	245
46	Alaska	226
47	Minnesota	201
48	Vermont	192
49	North Dakota	190
50	Oregon	167
51	Puerto Rico	82
52	Hawaii	56

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,337
2	New York	2,125
3	Massachusetts	2,017
4	Rhode Island	1,959
5	Mississippi	1,904
6	Connecticut	1,899
7	South Dakota	1,891
8	North Dakota	1,865
9	Louisiana	1,815
10	Arizona	1,622
11	Illinois	1,611
12	Pennsylvania	1,572
13	Michigan	1,494
14	Arkansas	1,489
15	New Mexico	1,452
16	Indiana	1,424
17	Iowa	1,408
18	Alabama	1,300
19	Nevada	1,271
20	Tennessee	1,260
21	South Carolina	1,237
22	Kansas	1,227
23	District of Columbia	1,224
24	Georgia	1,175
25	Texas	1,175
26	Florida	1,151
27	Missouri	1,125
28	Maryland	1,114
29	Minnesota	1,077
30	Delaware	1,054
31	Wisconsin	1,046
32	Montana	1,031
33	West Virginia	1,031
34	Nebraska	1,001
35	Colorado	959
36	Wyoming	950
37	Idaho	927
38	California	903
39	Ohio	899
40	Kentucky	802
41	North Carolina	800
42	Oklahoma	793
43	New Hampshire	707
44	Virginia	695
45	Puerto Rico	542
46	Washington	542
47	Utah	482
48	Oregon	438
49	Maine	398
50	Alaska	334
51	Vermont	269
52	Hawaii	229

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Arizona	25
2	Rhode Island	22
3	Pennsylvania	19
4	Alabama	17
5	Arkansas	16
6	California	16
7	Georgia	16
8	Mississippi	16
9	Wyoming	16
10	Nevada	14
11	Louisiana	13
12	Missouri	13
13	New Mexico	13
14	Texas	13
15	Indiana	12
16	Iowa	12
17	Oklahoma	12
18	Tennessee	12
19	West Virginia	12
20	Alaska	11
21	New Jersey	11
22	New York	10
23	Connecticut	9
24	Idaho	9
25	Kentucky	9
26	Massachusetts	9
27	Wisconsin	9
28	Michigan	8
29	North Carolina	8
30	South Carolina	8
31	Florida	7
32	Illinois	7
33	Maryland	7
34	Nebraska	7
35	New Hampshire	7
36	Virginia	7
37	North Dakota	6
38	Ohio	6
39	Washington	6
40	Kansas	5
41	Maine	5
42	Minnesota	4
43	Utah	4
44	Colorado	3
45	Delaware	3
46	District of Columbia	3
47	Oregon	3
48	Puerto Rico	3
49	Montana	2
50	South Dakota	2
51	Vermont	2
52	Hawaii	1

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	287,576	1	99
Dewey	South Dakota	232,858	2	99
Bent	Colorado	229,335	3	99
Lincoln	Arkansas	228,578	4	99
Lake	Tennessee	224,487	5	99
Davidson	Tennessee	116,894	254	91
Richland	South Carolina	78,851	1499	52
Orange	California	72,608	1798	42
York	South Carolina	72,212	1816	42
Pierce	Washington	36,541	2885	8

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Dickey	North Dakota	6,568	3	99
Grant	Nebraska	6,421	4	99
Iron	Wisconsin	6,330	5	99
Davidson	Tennessee	978	1900	39
Richland	South Carolina	936	1956	37
Orange	California	802	2168	30
York	South Carolina	733	2260	28
Pierce	Washington	482	2629	16

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.